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An unsupervised pattern (syndrome in traditional Chinese medicine) discovery algorithm based on association delineated by revised mutual information in chronic renal failure data
Chen, Jianxin1; Xi, Guangcheng1; Chen, Jing1; Zhen, Yisong2,3,4; Xing, Yanwei5; Wang, Jie5; Wang, Wei6
Source PublicationJOURNAL OF BIOLOGICAL SYSTEMS
2007-12-01
Volume15Issue:4Pages:435-451
SubtypeArticle
AbstractThe syndrome is the basic pathological unit and the key concept in traditional Chinese medicine (TCM), and the herbal remedy is prescribed according to the syndrome a patient catches. Nevertheless, few studies are dedicated to investigate the number of syndromes in chronic renal failure (CRF) patients and what these syndromes are. In this paper, we carry out a clinical epidemiology survey and obtain 601 CRF cases, including 72 symptoms in each report. Based on association delineated by mutual information, we propose a novel pattern discovery algorithm to discover syndromes, which probably have overlapped symptoms in TCM. A revised version of mutual information is presented here to discriminate positive and negative association. The algorithm self-organizedly discovers 16 effective patterns, each of which is verified manually by TCM physicians to recognize the syndrome it belongs to. The super-additivity of cluster by mutual information is proved and n-class association concept is introduced in our model to reduce computational complexity. Validation of the algorithm is performed by using the syndrome data and consolidated clinically to have 16 patterns. The results indicate that the algorithm achieves a high sensitivity with 96.48% and each classified pattern is of clinical significance. Therefore, we conclude that the algorithm provides an excellent solution to chronic renal failure problem in the context of traditional Chinese medicine.
KeywordMutual Information Association Measure Unsupervised Pattern Discovery Algorithm Clinical Epidemiology Survey Syndrome Traditional Chinese Medicine
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
Indexed BySCI
Language英语
WOS Research AreaLife Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology
WOS SubjectBiology ; Mathematical & Computational Biology
WOS IDWOS:000252216300002
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/9492
Collection09年以前成果
Affiliation1.Chinese Acad Sci, Inst Automat, Key Lab Complex Syst & Intelligence Sci, Beijing 100080, Peoples R China
2.Chinese Acad Sci, FuWai Hosp, Minist Educ,Key Lab Clin Cardiovasc Genet, Sinogerman Lab Mol Med, Beijing 100864, Peoples R China
3.Chinese Acad Sci, Cardiovasc Inst, Beijing 100864, Peoples R China
4.Peking Union Med Coll, Beijing, Peoples R China
5.Chinese Acad Chinese Med Sci, GuangAnMen Hosp, Beijing 100053, Peoples R China
6.Beijing Univ Chinese Med, Beijing 100029, Peoples R China
Recommended Citation
GB/T 7714
Chen, Jianxin,Xi, Guangcheng,Chen, Jing,et al. An unsupervised pattern (syndrome in traditional Chinese medicine) discovery algorithm based on association delineated by revised mutual information in chronic renal failure data[J]. JOURNAL OF BIOLOGICAL SYSTEMS,2007,15(4):435-451.
APA Chen, Jianxin.,Xi, Guangcheng.,Chen, Jing.,Zhen, Yisong.,Xing, Yanwei.,...&Wang, Wei.(2007).An unsupervised pattern (syndrome in traditional Chinese medicine) discovery algorithm based on association delineated by revised mutual information in chronic renal failure data.JOURNAL OF BIOLOGICAL SYSTEMS,15(4),435-451.
MLA Chen, Jianxin,et al."An unsupervised pattern (syndrome in traditional Chinese medicine) discovery algorithm based on association delineated by revised mutual information in chronic renal failure data".JOURNAL OF BIOLOGICAL SYSTEMS 15.4(2007):435-451.
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